CRCNS: Ontology-Based Multi-Scale Integration of the Autism Phenome

CRCNS：基于本体论的自闭症现象多尺度整合

• 批准号: 7778021
• 负责人: AMARENDRA K. DAS
• 金额: $ 34.52万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-04 至 2012-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7778021
• 关键词: Address; Area; Autistic Disorder; Biological; Bipolar Disorder; Brain; Brain Mapping; Cataloging; Catalogs; Categories; Classification; Collaborations; Communities; Complex; Data; Databases; Development; Disease; Educational process of instructing; Funding; Genes; Genetic; Genetic Markers; Genetic Research; Genomics; Genotype; Human Genome; Informatics; Knowledge; Literature; Logic; Major Depressive Disorder; Measurement; Mental disorders; Methodology; Methods; Modeling; Neurosciences; Neurosciences Research; Noise; Ontology; Pathway interactions; Phenotype; Principal Investigator; Publishing; Research; Research Activity; Research Personnel; Research Project Grants; Resource Development; Resources; Schizophrenia; Signal Transduction; Software Tools; Teaching Materials; Technology; Universities; Work; autism spectrum disorder; base; biomedical informatics; computer based Semantic Analysis; endophenotype; genetic analysis; genetic variant; genome-wide; genotyping technology; improved; knowledge base; multi-scale modeling; neuropathology; novel; phenome; phenomics; psychogenetics; relating to nervous system; theories; tool; trait

DESCRIPTION (provided by applicant): Intellectual Merits of Proposed Activities With completion of the sequencing of the human genome, finding genetic variants that predispose and regulate brain-related disorders has increasingly become a significant area of collective neuroscience research. With advancements in genotyping technologies and analytic methodologies, investigators are making progress towards finding biological determinants of neuropathology. These efforts, however, have not been as rapid or as successful as those for non-mental disorders, and much still remains unknown about causal pathways between genes and complex traits in common mental disorders, such as schizophrenia, major depression, bipolar disorder and autism. An oft-cited reason for this lack of progress is the under use of intermediate phenotypes, or endophenotypes, which arguably provide higher genetic signal-to-noise ratios than the use of disease categories themselves. When researchers have incorporated endophenotypes into genetic analyses, the categories have not been based on a shared, well-defined and standardized set of definitions, making comparisons across studies and replication of prior findings problematic. Furthermore, it is unclear how categories of endophenotype measurements can be coherently integrated into multi-scale models of neuropathology. Phenomics - the systematic cataloging of phenotypes on a genome-wide scale - has emerged as a scientific endeavor within psychiatric genetics to address this challenge. A critical limitation to its advancement, and thus to its ability to support genomics studies of brain-related disease, is the lack of available methods and tools for modeling, managing, and reasoning about endophenotypes. We propose to overcome this major impediment through the development of the Phenologue, a novel knowledge-based technology that can support collaborative efforts to acquire, manage, and reason about a disease phenome given experimental data and published findings. The project's research objectives are to (1) develop an ontology of endophenotypes that maps brain connectivity, neural deficits, and genetic markers into a subject domain theory; (2) develop logic-based methods to encode and classify endophenotypes based on multi-scale measurements; (3) create tools to acquire new endophenotypes and annotate phenotype-genotype findings in online resources such as published literature; and (4) develop query-elicitation methods that can evaluate hypotheses about the subject domain theory of endophenotypes using deductive inference. These efforts will be undertaken through a close collaboration of researchers in psychiatric genetics, Semantic Web technologies, and first-order reasoning. Broader Impacts of Proposed Activities The research team will use the Phenologue to integrate data and knowledge from multiple lines of research on autism spectrum disorder. Thus, a broader objective of the activities proposed in this collaborative neuroscience project is to help investigators develop a coherent and formal understanding of the genetic underpinnings of this heterogeneous condition. The proposed project will build upon current NIH-funded efforts to create an autism ontology for the National Database for Autism Research (http://ndar.nih.gov), and the methods can be made accessible to users of this resource. The Principal Investigator will incorporate work from the proposed research on the use of ontologies and logic in scientific resource development into the teaching material of a graduate-level biomedical informatics course he offers at Stanford University. In addition, the investigative team will make software tools developed through the proposed project directly available to the other psychiatric genetics research communities, and will disseminate the proposed methods to similar informatics collaborations on brain-related disorders.

描述（由申请人提供）：拟议活动的智力优点随着人类基因组的测序完成，发现易感和调节脑相关疾病的遗传变异已越来越成为集体神经科学研究的重要领域。随着基因分型技术和分析方法的进步，研究人员正在进步寻找神经病理学的生物学决定因素。然而，这些努力并不像非精神疾病那样快速或成功，并且对于基因与普通精神障碍中的复杂特征（例如精神分裂症，严重抑郁症，双相情感障碍和自闭症）中的因果途径和复杂性状的原因仍然不清楚。缺乏进步的经常引用的原因是使用中间表型或内型型的使用，可以说，与疾病类别本身的使用相比，它可以说提供更高的遗传信噪比。当研究人员将内型型纳入遗传分析时，这些类别尚未基于共享，定义明确和标准化的定义集，从而在研究中进行比较和先前发现的复制问题。此外，尚不清楚如何将内型型测量类别连贯地整合到多尺度的神经病理模型中。现象学 - 在全基因组量表上的系统分类 - 已成为精神遗传学中的一项科学努力，以应对这一挑战。其进步的关键局限性，从而支持其支持脑相关疾病的基因组学研究的能力，是缺乏用于建模，管理和推理内型型的可用方法和工具。我们建议通过发展现实的发展来克服这一主要障碍，这是一种基于知识的新技术，可以支持在实验数据和发表的发现的情况下，以获取，管理和理性的疾病现象的协作努力。该项目的研究目标是（1）开发一个本体，将大脑连通性，神经缺陷和遗传标记映射到主题领域理论中； （2）开发基于逻辑的方法，以基于多尺度测量值编码和对内型型进行分类； （3）创建工具以获取新的内型型和注释在线资源（例如已发表文献）中的表型基因型发现； （4）开发查询引导方法，可以使用演绎推理评估有关主型型的主题领域理论的假设。这些努力将通过在精神遗传学，语义Web技术和一阶推理方面的研究人员的密切合作来实现。拟议活动的更广泛的影响研究团队将使用现实情况将数据和知识整合到自闭症谱系障碍的多种研究方面。因此，在此协作神经科学项目中提出的活动的更广泛的目标是帮助研究人员对这种异质状况的遗传基础有一致而正式的理解。拟议的项目将基于NIH资助的当前努力，为国家自闭症研究数据库创建自闭症本体（http://ndar.nih.gov），并且可以使该资源的用户可以访问这些方法。首席研究人员将纳入有关科学资源开发中使用本体和逻辑的拟议研究中的工作，并将他在斯坦福大学提供的研究生水平的生物医学信息学课程中。此外，调查团队将通过直接可用于其他精神遗传学研究社区的拟议项目开发软件工具，并将提出的方法传播到有关大脑相关疾病的类似信息学合作。